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Publisher DOI: 10.3390/s19092094
Title: Learning environmental field exploration with computationally constrained underwater robots : Gaussian processes meet stochastic optimal control
Language: English
Authors: Dücker, Daniel-André  
Geist, Andreas René 
Kreuzer, Edwin 
Solowjow, Eugen 
Keywords: autonomous exploration; environmental field monitoring; Gaussian processes; Gaussian Markov random fields; Kalman filtering; stochastic optimal control
Issue Date: 6-May-2019
Publisher: Multidisciplinary Digital Publishing Institute
Source: Sensors 19 (9): 2094 (2019)
Abstract (english): 
Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.
DOI: 10.15480/882.2267
ISSN: 1424-8220
Journal: Sensors 
Other Identifiers: doi: 10.3390/s19092094
Institute: Mechanik und Meerestechnik M-13 
Document Type: Article
Project: Open Access Publizieren 2018 - 2019 / TU Hamburg 
Zustandsschätzung von Strömungsfeldern und Quellfindung mittels dynamisch positionierter Unterwasser-Sensorknoten. 
Dezentrale kooperative Exploration von nichtstationären räumlich und zeitlich verteilten Feldern mit autonomen Unterwasserfahrzeugen 
More Funding information: Deutsche Forschungsgemeinschaft (DFG)
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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